1. Field of Invention
The disclosed invention generally pertains to the field of credit risk scores, and more specifically, to a system and method for automatically compensating an individual's credit risk score for macroeconomic data.
2. Description of the Related Art
In the United States, a credit risk score, or simply credit score, represents the creditworthiness of an individual. Lenders, such as banks and credit card companies, use an individual's credit score to evaluate the potential risk of lending money to the individual. In other words, lenders will use credit scores to determine who qualifies for a loan, at what interest rate and credit limits, and who does not.
The most widely known credit score in the United States is the Fair Isaac Corporation score (FICO). This score is calculated by applying statistical methods and data analysis, developed by Fair Isaac, to information in an individual's credit file and will range from 300 to 850. In addition, there are three major credit reporting agencies, Equifax, Experian, and TransUnion, who are often referred to as the “credit bureaus” and also calculate their own credit scores. Credit scores for the same individual will differ among the three agencies due to the statistical methods employed by each agency, what information is used, and what weight is given to the information.
In 2006, the credit bureaus introduced VantageScore® (registered trademark of VantageScore Solutions, LLC, Wilmington, Del.) to provide an alternative to the traditional scores discussed above. VantageScore uses a different range than the traditional FICO score, i.e., 501 to 990. An individual's VantageScore® may still differ from agency to agency; however, this difference is not due to the credit score model but due to differences in how information is reported to and stored at the three agencies.
The goal of any credit score model is to be able to identify as many people as possible who are good credit risks and eliminate those who are bad credit risks. Thus, the value of a credit score to lenders is its ability to predict an individual's future creditworthiness. Typically, the information that is used to calculate an individual's credit score is account information, such as credit cards, automobile loans, student loans, and mortgages, public records, such as tax liens and bankruptcies, and inquires, such as requests by lenders to view the individual's credit. In addition, various factors are considered and weighted such as punctuality of payment in the past, the amount of debt, length of credit history, types of credit used, and recent search for credit and/or amount of credit obtained recently.
However, a problem that exists with traditional risk scores is over time they tend to lose accuracy due to macroeconomic changes. As a result, lenders, who have determined that 5% of individuals with a score of 500 will default on a loan, for example, will discover that due to a decline in the economy, they need to adjust the score to 550 in order not to accept greater risk. Therefore, lenders must validate the performance and distribution of their model on a regular basis and adjust strategies and/or develop their model on a regular basis in order to compensate for economic changes in the use of credit risk models for decision purposes. This is time and resource intensive, both from the standpoint of performing the analysis, e.g., collecting historical data and running the analysis to interpret the validation results, and from an operational standpoint, e.g., changing score cut offs or embedding new scores in existing processes and training personnel to understand the new policy. In addition, while distributions can be run on the current economic cycle, validations by definition require looking at scores over a historical time frame. In other words, validations require looking at the economic conditions at the time when individuals, who have defaulted on a loan, applied for the loan. Therefore, the current economic cycle is not applicable.
Thus, a need exists to provide a mechanism by which the model can essentially adjust for current economic conditions, in which the adjustment is based on correlated econometric factors determined by analysis of the historical impact of these factors on score distributions and performance. Such an automatic adjustment will allow model strategies to keep pace with economic changes without requiring lenders to validate the performance and distribution of the model and adjust strategies and/or develop the model as frequently as otherwise.